By now, you’ve heard of generative AI tools like ChatGPT, DALL-E, and GitHub Copilot, among others. They’re gaining widespread interest thanks to the fact they allow anyone to create content from email subject lines to code functions to artwork in a matter of moments.
Generative AI has sparked curiosity among companies in Singapore who recognize its potential to drive innovation, optimize business processes, improve customer experience, and enhance products and services. The startup ecosystem in Asia in particular has been proactive in its approach to generative AI, with many companies already experimenting with the technology.
The potential to revolutionize content creation across various industries makes it important to understand what generative AI is and how it can be used. Let’s explore how generative AI works, real-world applications, and how it’s already changing the way software developers work.
What is generative AI?
At a high level, generative AI refers to a category of AI models and tools designed to create new content, such as text, images, videos, music, or code. Generative AI uses a variety of techniques — including neural networks and deep learning algorithms — to identify patterns and generate new outcomes based on them. Organizations and people, which include software developers and engineers, are increasingly looking to generative AI tools to create content, code, images, and more.
What is generative AI used for?
Traditional AI systems are trained on large amounts of data to identify patterns, and they’re capable of performing specific tasks that can help people and organizations. But generative AI goes one step further by using complex systems and models to generate new or novel outputs in the form of an image, text, or audio based on natural language prompts.
Generative AI models and applications can, for example, be used for text, image, video, data, programming code generation, and language translation. From a developer’s standpoint, rather than scouring the internet or developer communities for help with code examples, generative AI models can be used to help generate new programming code with natural language prompts, complete partially written code with suggestions, or even translate code from one programming language to another. For example, at a simple level, GitHub Copilot uses OpenAI’s Codex model to offer code suggestions right from a developer’s editor.
How does generative AI work?
Generative AI models work by using neural networks to identify patterns from large sets of data, then generate new and original data or content.
But what are neural networks? In simple terms, they use interconnected nodes that are inspired by neurons in the human brain. These networks are the foundation of machine learning and deep learning models, which use a complex structure of algorithms to process large amounts of data such as text, code, or images. Training these neural networks involves adjusting the weights or parameters of the connections between neurons to minimize the difference between predicted and desired outputs, which allows the network to learn from mistakes and make more accurate predictions based on the data.
The real-world applications of generative AI
The impact of generative AI is quickly becoming apparent — but it’s still in its early days. Despite this, we’re already seeing a proliferation of applications, products, and open-source projects that are using generative AI models to achieve specific outcomes for people and organizations – and yes, that includes developers too.
Generative AI also empowers developers in two aspects: coding and enabling accessibility to developers with disabilities.
Coding: New and seasoned developers alike can utilize generative AI to improve their coding processes. Generative AI coding tools can help automate some of the more repetitive tasks, like testing, as well as complete code or even generate brand new code. For example, GitHub Copilot uses generative AI to provide developers with code suggestions. The latest GitHub Copilot X brings generative AI to more of the developer experience across the editor, pull requests, documentation, CLI, and more.
Accessibility: Generative AI has the potential to greatly impact and improve accessibility for folks with disabilities through a variety of modalities, such as speech-to-text transcription, text-to-speech audio generation, or assistive technologies. One of the most exciting capabilities of the GitHub Copilot tool is its voice-activated capabilities that allow developers who find it difficult to use a keyboard to code with their voice. By leveraging the power of generative AI, these types of tools are paving the way for a more inclusive and accessible future in technology.
Obstacles in building generative AI models
While generative AI models are being used to power applications, there are two key challenges any organization building or using one will face.
Firstly, generative AI requires significant compute resources, powerful GPUs, and large amounts of memory. This type of hardware is costly, which in turn also creates a barrier to entry for a lot of individuals or organizations to build in-house solutions. Secondly, training generative AI models to create accurate outputs also requires large amounts of high-quality data. If training data is biased or incomplete, the models may generate content that is inaccurate (that’s why generative AI design tools have a particularly hard time recreating human hands) or not useful.
The future of software development
Generative AI is bringing in a new mode of interaction — and it doesn’t just alleviate the tedious parts of software development. Critically, it allows developers to be more creative, feel empowered to tackle big problems, and model large, complex solutions in ways they couldn’t before. From increasing productivity and offering alternative solutions to helping developers build new skills — like learning a new language or framework, or even writing clear comments and documentation — there are so many reasons to be excited about the next wave of software development. This is only the beginning.
Damian Brady leads the Developer Advocacy team at GitHub and loves all things DevOps. Formerly a Cloud Advocate at Microsoft for 4 years, Damian has a 20+ year background in software development and consulting in a broad range of industries. Damian regularly speaks at conferences, User Groups, and other events around the world. Most of the time you’ll find him talking to developers, IT Pros, and data scientists to help them get the most out of their DevOps and MLOps strategies.
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